Meet the past SIB Awards Laureates – Jakob Ruess

I was a PhD student who had just finished the first year! Naturally, receiving such a prestigious award so early in my career felt incredible. It really motivated me and surely, it also had an influence on the fact that I decided, and had the possibility, to pursue an academic career. The award was for a paper in which we developed a method that infers stochastic kinetic models from single-cell gene expression data and used it to characterize cell-to-cell variability in the response to osmotic stress in yeast. What I do these days is in many aspects still very similar to this type of work, but biology is moving so fast that the challenge always changes and renews itself.

Among the "three Bs" of the C3BI, my work qualifies best as "Integrative Biology". I originally come from a statistics and probability theory background, but I am not so much into the “large data sets” and “machine learning” trends of bioinformatics. What really motivates me, is the fascination of developing mathematical models that allow us to explain and simulate how life is working dynamically and to also use these models in a synthetic biology context to design living systems ourselves. For now, this is only realistically achievable on a small scale, so we typically focus on studying some specific biochemical processes inside cells. For instance, we are currently working on using stochastic processes to model simple synthetic gene networks such as the "toggle switch" or the "repressilator" – simple networks, in which genes are either active (switched on) or inactive (switched off). What is really great is that we have our own biology lab at the Pasteur Institute and students who can build such networks to generate detailed data on their dynamics. Recently, we have also started to automate our experimental platforms. This sounds only like a mere technical challenge, but if you think about it a bit more and ask yourself what you can do with an intelligent and autonomous microscopy platform - which is for instance capable of optogenetically (Ed. note: using light) inducing gene expression in single cells and measuring it - you realize that an entire new world of possible experiments opens up. The platform stops being just a device to carry out your experiment and instead becomes an interface to interact with cells. I know that it sounds a bit like science fiction, but the idea is really to understand biology by developing computer programs that communicate with gene networks in real time. This will probably be one of my main directions in the next years, if I manage to find ambitious students or postdocs who are not afraid to depart on such a new scientific topic.

I do not really want to point to any one single discovery. What really advances us is the flow of science as a whole and bioinformatics is one of the engines that keeps this flow going. As I see it, thinking in terms of breakthrough results is born out of our human desire to categorize and quantify things, but it doesn't really do the real scientific process justice.